A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network
Accurate real-time prediction of link quality is crucial for enhancing the reliable responsiveness of wearable devices within Wireless Wearable Sensor Networks (WWSNs). Specifically, the Signal-to-Noise Ratio (SNR), a pivotal parameter for predicting link quality, exhibits complex temporal character...
Main Authors: | Minghu Zha, Li Zhu, Yunyun Zhu, Jun Li, Tao Hu |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-06-01
|
Series: | Intelligent Systems with Applications |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324000395 |
Similar Items
-
Genetic screening of SCNN1B and SCNN1G genes in early-onset hypertensive patients helps to identify Liddle syndrome
by: Kun-Qi Yang, et al.
Published: (2018-02-01) -
Confidence with confidence intervals
by: Thomas Ravi, et al.
Published: (1997-01-01) -
The Shortest Clopper–Pearson Randomized Confidence Interval for Binomial Probability
by: Wojciech Zieliński
Published: (2017-01-01) -
SCNN1A Overexpression Correlates with Poor Prognosis and Immune Infiltrates in Ovarian Cancer
by: Lou J, et al.
Published: (2022-02-01) -
Analysis of variance via confidence intervals /
by: 306322 Bird, Kevin D.
Published: (2004)